Citation

Bhuiyan, Md Roman and Abdullah, Junaidi and Hashim, Noramiza and Farid, Fahmid Al and Isa, Wan Noorshahida Mohd and Uddin, Jia and Karim, Hezerul Abdul and Mansor, Sarina and Balaganesh, Duraisamy and Sarker, Md Tanjil (2025) Optical flow and deep learning-based anomaly detection for hajj pilgrimage crowd monitoring. Signal, Image and Video Processing, 19 (9). ISSN 1863-1703

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Abstract

In computer vision, identifying anomalies in crowded Hajj situations is challenging due to severe inter-object occlusions, varied crowd concentrations, and complex dynamics of the human mass. Two primary goals are addressed by this FCNN-based architecture: feature representation and wrong movement outlier identification. We proposed a crowd anomaly hajj monitor (CAHM) method using video sequences of crowded scenes to identify and localize abnormal behavior. The main contribution of our approach is the combination of anomaly detection-based optical flow features and classification based on spatial-temporal features using fully convolutional neural networks (FCNN), which has never been achieved in the past based on our extensive research in this domain. Using FCNN and spatial-temporal data, a pre-trained supervised FCNN detects (global) anomalies in Hajj crowded scenes. Additionally, this research intends to develop a new dataset based on the Hajj pilgrimage scenario in order to solve the issues. This architecture enables us, through spatial-temporal convolutions, to capture features in both spatial and time dimensions and to extract knowledge about the presence and motion of features encrypted in continuous frames. Two primary goals are addressed by this FCNN-based architecture: feature representation and cascade outlier identification. The suggested technique outperforms current methods in terms of detection and localization accuracy, as shown in the experimental results on the benchmark datasets. We employ the UCSD and Subway dataset in our study and present the Hajj-Crowd-2021 as a new video dataset. Extensive testing on the proposed Hajj-Crowd-2021 anomaly dataset demonstrates that it provides cutting-edge recognition performance and outstanding durability in crowd analysis. To verify our work, we used the available datasets to compare the proposed model to current models. In all of these situations, our model outperforms. Crowd analysis, for instance, improves classification accuracy by achieving 96% in highly crowded crowd situations. Furthermore, crowd anomaly analysis improves classification accuracy for the UCSD, Ped1, and Ped2 datasets for extremely congested crowd situations by 89%, 83%, and 85%, respectively. On the other hand, our method has improved classification accuracy for the Subway dataset by 88% for the exit, and 86% for the entrance case.

Item Type:	Article
Uncontrolled Keywords:	Crowd Anomaly Classification · FCNN · Optical Flow · Hajj Crowd Video Dataset
Subjects:	Q Science > QC Physics > QC350-467 Optics. Light
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	01 Jul 2025 01:54
Last Modified:	01 Jul 2025 01:54
URII:	http://shdl.mmu.edu.my/id/eprint/14203

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